Using a Trained QRF Model via Hugging Face Hub

This example demonstrates how to download a trained quantile regression forest (QRF) model from Hugging Face Hub and use it to estimate new quantiles. In this scenario, a QRF has been trained with default parameters on a train-test split of the California housing dataset and uploaded to Hugging Face Hub. The model is downloaded and used to perform inference across several quantiles for each dataset sample. The results are visualized by the latitude and longitude of each sample. The model used is available on Hugging Face Hub here.

import os
import pickle
import shutil
import tempfile

import altair as alt
import numpy as np
import pandas as pd
from sklearn import datasets
from skops import hub_utils

import quantile_forest
from quantile_forest import RandomForestQuantileRegressor

alt.data_transformers.disable_max_rows()

token = "<Hugging Face Access Token>"
repo_id = "quantile-forest/california-housing-example"
load_existing = True

random_state = np.random.RandomState(0)
quantiles = np.linspace(0, 1, num=5, endpoint=True).round(2).tolist()
sample_frac = 1


def fit_and_upload_model(token, repo_id, local_dir="./local_repo", random_state=None):
    """Function used to fit the model and upload it to Hugging Face Hub."""
    from pathlib import Path

    from sklearn.metrics import (
        mean_absolute_percentage_error,
        mean_squared_error,
        median_absolute_error,
        r2_score,
    )
    from sklearn.model_selection import train_test_split
    from skops import card

    # Load the California Housing dataset.
    X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)

    # Fit the model.
    qrf = RandomForestQuantileRegressor(random_state=random_state).fit(X_train, y_train)

    # Save the model to a file.
    model_filename = "model.pkl"
    with open(model_filename, mode="bw") as f:
        pickle.dump(qrf, file=f)

    # Prepare model repository.
    if os.path.exists(local_dir):
        shutil.rmtree(local_dir)
    os.mkdir(local_dir)

    # Initialize the repository.
    hub_utils.init(
        model=model_filename,
        requirements=[f"quantile-forest={quantile_forest.__version__}"],
        dst=local_dir,
        task="tabular-regression",
        data=X_train,
    )

    # Create a model card.
    model_card = card.Card(qrf, metadata=card.metadata_from_config(Path(local_dir)))
    model_card.metadata.library_name = "quantile-forest"
    model_card.metadata.license = "apache-2.0"
    model_card.metadata.tags = [
        "quantile-forest",
        "sklearn",
        "skops",
        "tabular-regression",
        "quantile-regression",
        "uncertainty-estimation",
        "prediction-intervals",
    ]
    model_description = (
        "This is a RandomForestQuantileRegressor trained on the California housing dataset."
    )
    limitations = "This model is not ready to be used in production."
    training_procedure = (
        "The model was trained using default parameters on a standard train-test split."
    )
    get_started_code = """<details>
<summary> Click to expand </summary>

```python
import pickle
with open(qrf_pkl_filename, 'rb') as file:
    qrf = pickle.load(file)
```

</details>"""
    model_card.add(
        **{
            "Model description": model_description,
            "Model description/Intended uses & limitations": limitations,
            "Model description/Training Procedure": training_procedure,
            "How to Get Started with the Model": get_started_code,
        }
    )

    # Add performance metrics to the model card.
    y_pred = qrf.predict(X_test)
    mape = mean_absolute_percentage_error(y_test, y_pred)
    mdae = median_absolute_error(y_test, y_pred)
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)
    model_card.add_metrics(
        **{
            "Mean Absolute Percentage Error": mape,
            "Median Absolute Error": mdae,
            "Mean Squared Error": mse,
            "R-Squared": r2,
        },
    )

    # Save the model card locally.
    model_card.save(Path(local_dir) / "README.md")

    # Create the repository on the Hugging Face Hub if it does not exist.
    # Push the model to the repository.
    try:
        hub_utils.push(
            repo_id=repo_id,
            source=local_dir,
            token=token,  # personal token to be downloaded from Hugging Face
            commit_message="Model commit",
            create_remote=True,
        )
    except Exception as e:
        print(f"Error pushing model to Hugging Face Hub: {e}")

    os.remove(model_filename)
    shutil.rmtree(local_dir)


if not load_existing:
    fit_and_upload_model(token, repo_id, random_state=random_state)

# Download the repository locally and load the fitted model.
model_filename = "model.pkl"
local_dir = "./local_repo"
with tempfile.TemporaryDirectory() as local_dir:
    hub_utils.download(repo_id=repo_id, dst=local_dir)
    with open(f"{local_dir}/{model_filename}", "rb") as file:
        qrf = pickle.load(file)

# Fetch the California Housing dataset and estimate quantiles.
X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True)
y_pred = qrf.predict(X, quantiles=quantiles) * 100_000  # predict in dollars

df = (
    pd.DataFrame(y_pred, columns=quantiles)
    .reset_index()
    .sample(frac=sample_frac, random_state=random_state)
    .melt(id_vars=["index"], var_name="quantile", value_name="value")
    .merge(X[["Latitude", "Longitude", "Population"]].reset_index(), on="index", how="right")
)


def plot_quantiles_by_latlon(df, quantiles, color_scheme="cividis"):
    """Plot quantile predictions on California Housing dataset by lat/lon."""
    # Slider for varying the displayed quantile estimates.
    slider = alt.binding_range(
        name="Predicted Quantile: ",
        min=0,
        max=1,
        step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1),
    )

    quantile_val = alt.param(name="quantile", value=0.5, bind=slider)

    chart = (
        alt.Chart(df)
        .add_params(quantile_val)
        .transform_filter("datum.quantile == quantile")
        .mark_circle()
        .encode(
            x=alt.X(
                "Longitude:Q",
                axis=alt.Axis(tickMinStep=1, format=".1f"),
                scale=alt.Scale(zero=False),
                title="Longitude",
            ),
            y=alt.Y(
                "Latitude:Q",
                axis=alt.Axis(tickMinStep=1, format=".1f"),
                scale=alt.Scale(zero=False),
                title="Latitude",
            ),
            color=alt.Color("value:Q", scale=alt.Scale(scheme=color_scheme), title="Prediction"),
            size=alt.Size("Population:Q"),
            tooltip=[
                alt.Tooltip("index:N", title="Row ID"),
                alt.Tooltip("Latitude:Q", format=".2f", title="Latitude"),
                alt.Tooltip("Longitude:Q", format=".2f", title="Longitude"),
                alt.Tooltip("value:Q", format="$,.0f", title="Predicted Value"),
            ],
        )
        .properties(
            title="Quantile Predictions on the California Housing Dataset",
            height=650,
            width=650,
        )
    )
    return chart


chart = plot_quantiles_by_latlon(df, quantiles)
chart